Global Shallow-Water Bathymetry From Satellite Ocean Color Data

In this study, the goal is to estimate the sedimentation on the bottom bed of Lake Taihu using numerical simulation combined with geostationary satellite ocean color data. A two-dimensional (2D) model that couples the dynamics of shallow water and sediment transport is presented. The shallow water equations are solved using a semi-implicit finite difference method with an Alternating Direction Implicit (ADI) method. Suspended sediment transport is simulated by solving the general convection-diffusion equation with resuspension and deposition terms using a second-order explicit central difference method in space and two-step Adams–Bashforth method in time. Moreover, the total suspended particulate matter (TSM) is retrieved by the world’s first geostationary satellite ocean color sensor Geostationary Ocean Color Imager (GOCI) using atmospheric correction algorithm for turbid waters using ultraviolet wavelengths (UV-AC) and regional empirical TSM algorithm. The 2D model and GOCI-retrieved TSM are applied to study the sediment transport and sedimentation in Lake Taihu. Validation results show rationale TSM concentration retrieved by GOCI, and the simulated TSM concentrations are consistent with GOCI observations. In addition, simulated sedimentation results reveal the dangerous locations that must be observed and desilted.

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Pan-Arctic optical characteristics of colored dissolved organic matter: Tracing dissolved organic carbon in changing Arctic waters using satellite ocean color data

Remote Sensing of Environment ◽

10.1016/j.rse.2017.08.009 ◽

2017 ◽

Vol 200 ◽

pp. 89-101 ◽

Cited By ~ 19

Author(s):

Atsushi Matsuoka ◽

Emmanuel Boss ◽

Marcel Babin ◽

Lee Karp-Boss ◽

Mark Hafez ◽

...

Keyword(s):

Organic Matter ◽

Organic Carbon ◽

Dissolved Organic Matter ◽

Dissolved Organic Carbon ◽

Ocean Color ◽

Optical Characteristics ◽

Colored Dissolved Organic Matter ◽

Color Data ◽

Satellite Ocean Color

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Improvement of Atmospheric Correction of Satellite Sentinel-3/OLCI Data for Oceanic Waters in Presence of Sargassum

Remote Sensing ◽

10.3390/rs14020386 ◽

2022 ◽

Vol 14 (2) ◽

pp. 386

Author(s):

Léa Schamberger ◽

Audrey Minghelli ◽

Malik Chami ◽

François Steinmetz

Keyword(s):

Near Infrared ◽

Ocean Color ◽

Atmospheric Correction ◽

Correction Algorithm ◽

Infrared Band ◽

Economic Issues ◽

Optical Signature ◽

Color Data ◽

The Caribbean ◽

Satellite Ocean Color

The invasive species of brown algae Sargassum gathers in large aggregations in the Caribbean Sea, and has done so especially over the last decade. These aggregations wash up on shores and decompose, leading to many socio-economic issues for the population and the coastal ecosystem. Satellite ocean color data sensors such as Sentinel-3/OLCI can be used to detect the presence of Sargassum and estimate its fractional coverage and biomass. The derivation of Sargassum presence and abundance from satellite ocean color data first requires atmospheric correction; however, the atmospheric correction procedure that is commonly used for oceanic waters needs to be adapted when dealing with the occurrence of Sargassum because the non-zero water reflectance in the near infrared band induced by Sargassum optical signature could lead to Sargassum being wrongly identified as aerosols. In this study, this difficulty is overcome by interpolating aerosol and sunglint reflectance between nearby Sargassum-free pixels. The proposed method relies on the local homogeneity of the aerosol reflectance between Sargassum and Sargassum-free areas. The performance of the adapted atmospheric correction algorithm over Sargassum areas is evaluated. The proposed method is demonstrated to result in more plausible aerosol and sunglint reflectances. A reduction of between 75% and 88% of pixels showing a negative water reflectance above 600 nm were noticed after the correction of the several images.

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